Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	134,149
2	South Dakota	131,829
3	Rhode Island	127,709
4	Utah	119,564
5	Tennessee	115,890
6	Arizona	115,128
7	Iowa	110,456
8	Oklahoma	110,312
9	Arkansas	109,188
10	Wisconsin	108,602
11	Nebraska	107,494
12	South Carolina	105,901
13	Alabama	104,654
14	Kansas	104,096
15	Mississippi	102,125
16	Indiana	101,642
17	Idaho	100,169
18	New Jersey	99,286
19	Nevada	98,114
20	Illinois	97,335
21	Montana	97,270
22	Georgia	96,693
23	Wyoming	96,620
24	Texas	95,598
25	Kentucky	95,576
26	Delaware	95,544
27	Louisiana	95,211
28	Missouri	94,764
29	Florida	94,396
30	New York	93,741
31	California	92,459
32	New Mexico	90,847
33	Minnesota	90,515
34	Massachusetts	90,275
35	North Carolina	86,657
36	Ohio	86,077
37	Connecticut	85,129
38	Alaska	84,147
39	Colorado	79,359
40	Pennsylvania	78,542
41	West Virginia	77,700
42	Virginia	71,496
43	Michigan	71,313
44	Maryland	66,852
45	District of Columbia	61,876
46	New Hampshire	60,335
47	Washington	47,446
48	Puerto Rico	43,473
49	Oregon	38,617
50	Maine	36,593
51	Vermont	29,191
52	Hawaii	20,566

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New York	611
2	Michigan	513
3	New Jersey	481
4	Rhode Island	393
5	Connecticut	359
6	New Hampshire	315
7	Pennsylvania	309
8	Massachusetts	307
9	Florida	251
10	South Dakota	245
11	Iowa	240
12	Alaska	238
13	Minnesota	238
14	Colorado	229
15	Montana	228
16	North Dakota	217
17	West Virginia	213
18	Delaware	208
19	Tennessee	203
20	Idaho	201
21	Vermont	200
22	South Carolina	197
23	Maryland	180
24	Illinois	179
25	North Carolina	176
26	Kentucky	170
27	Georgia	167
28	Virginia	167
29	Texas	156
30	Ohio	141
31	Missouri	140
32	Nebraska	140
33	District of Columbia	135
34	Maine	135
35	Wisconsin	133
36	Indiana	127
37	Utah	125
38	Mississippi	113
39	Washington	112
40	Louisiana	111
41	Wyoming	109
42	New Mexico	95
43	Alabama	92
44	Nevada	90
45	Arkansas	88
46	Oklahoma	88
47	Oregon	85
48	Kansas	76
49	California	65
50	Arizona	57
51	Puerto Rico	50
52	Hawaii	48

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,739
2	New York	2,534
3	Massachusetts	2,467
4	Rhode Island	2,460
5	Mississippi	2,347
6	Arizona	2,318
7	Connecticut	2,205
8	South Dakota	2,178
9	Louisiana	2,166
10	Alabama	2,142
11	North Dakota	1,964
12	Pennsylvania	1,950
13	Indiana	1,928
14	New Mexico	1,868
15	Illinois	1,849
16	Arkansas	1,846
17	Iowa	1,809
18	South Carolina	1,764
19	Georgia	1,712
20	Tennessee	1,712
21	Michigan	1,701
22	Nevada	1,693
23	Kansas	1,675
24	Texas	1,655
25	Delaware	1,577
26	Ohio	1,572
27	Florida	1,534
28	District of Columbia	1,492
29	California	1,477
30	West Virginia	1,464
31	Missouri	1,450
32	Maryland	1,359
33	Kentucky	1,343
34	Montana	1,342
35	Wisconsin	1,248
36	Oklahoma	1,225
37	Minnesota	1,222
38	Wyoming	1,200
39	Virginia	1,188
40	Nebraska	1,178
41	North Carolina	1,146
42	Idaho	1,095
43	Colorado	1,069
44	New Hampshire	903
45	Washington	694
46	Puerto Rico	659
47	Utah	651
48	Oregon	564
49	Maine	543
50	Alaska	408
51	Vermont	357
52	Hawaii	321

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	11
2	Montana	6
3	Nebraska	6
4	Arizona	5
5	California	5
6	Georgia	5
7	Massachusetts	5
8	New Jersey	5
9	New York	5
10	North Carolina	5
11	Oklahoma	5
12	Alabama	4
13	Missouri	4
14	Nevada	4
15	Tennessee	4
16	Texas	4
17	Iowa	3
18	Kansas	3
19	Louisiana	3
20	Michigan	3
21	Mississippi	3
22	New Mexico	3
23	Pennsylvania	3
24	South Carolina	3
25	Arkansas	2
26	Florida	2
27	Minnesota	2
28	New Hampshire	2
29	North Dakota	2
30	Utah	2
31	Vermont	2
32	Washington	2
33	West Virginia	2
34	Alaska	1
35	Connecticut	1
36	Delaware	1
37	District of Columbia	1
38	Idaho	1
39	Illinois	1
40	Indiana	1
41	Maryland	1
42	Ohio	1
43	South Dakota	1
44	Wisconsin	1
45	Wyoming	1
46	Colorado	0
47	Hawaii	0
48	Maine	0
49	Oregon	0
50	Puerto Rico	0
51	Rhode Island	0
52	Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	350,767	1	99
Chattahoochee	Georgia	314,477	2	99
Bent	Colorado	266,093	3	99
Lincoln	Arkansas	243,781	4	99
Dewey	South Dakota	243,551	5	99
Davidson	Tennessee	134,821	207	93
Richland	South Carolina	104,373	1042	66
York	South Carolina	100,655	1215	61
Orange	California	83,558	2007	36
Pierce	Washington	45,877	2905	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Galax city	Virginia	7,878	3	99
Foard	Texas	7,792	4	99
Emporia city	Virginia	7,669	5	99
Orange	California	1,469	1782	43
Davidson	Tennessee	1,279	2008	36
York	South Carolina	1,271	2022	35
Richland	South Carolina	1,260	2035	35
Pierce	Washington	666	2726	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.